Wildfires are occurring throughout the world, causing more damage to the plant and animal species, humans, and the environment. Fire danger indices are useful for forecasting fire danger and these indices are an integration of both static and dynamic indices. The static indicators, such as vegetation, topographic characteristics, etc., are constant over the study area and are variables that promote the ignition of fires and, therefore, useful for understanding fire patterns and distribution in the study area. In this study, The Static fire danger index (SFDI) is generated using MODIS Land cover type (MCD12Q1), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Open Street Map datasets by applying Random Forest (RF) algorithm. Random forest (RF) is a machine learning algorithm, which can automatically select important variables and flexibly evaluate the complex interaction between variables. The MODIS TERRA and AQUA active fire points (MCD14) during 2011-2017 have been used to train the RF algorithm and fire probability maps are generated for the years 2018 and 2019. The fire probability maps are categorized into 5 fire danger classes, i.e., very low, low, medium, high, and very high based on the RF prediction probability values. The active fire points (MCD14) have been used for validating the SFDI and accuracy is found to be 85.74% and 87.91% for the years 2018 and 2019 respectively. Thus, the machine learning algorithm is successfully applied for generating the wildfire susceptibility maps.

The abundance of red squirrels can be much higher in urban habitats than in natural forests. One of the most significant factors that influence density is food availability. Aim of our study was to compare two populations with high (approx. 2 ind./ha) and low (0.29 ind./ha) population density inhabiting the same city, but different habitats: one natural forest reserve, closed for public and second busy urban park, where squirrels are often fed by people. We wanted to determine how these two populations differed in terms of health, body condition, sexual activity, and stress level. We conducted two trapping sessions: in 2012-2013 and in 2018-2020. In total, during first trapping we trapped and ear-tagged 18 individuals in forest reserve and 45 individuals in urban park, during second trapping 36 individuals in forest reserve and 107 individuals in urban park. Our very first results show that squirrels in the forest had, in all seasons, on average higher body mass which may suggest better body condition. In turn, squirrels inhabiting urban park, started their year reproductive period earlier, which may be driven by year-round access to supplementary feeding. Moreover, contact with human was more stressful for squirrels from forest – breath rate of trapped squirrels was significantly higher. This study may be a proof that two populations inhabiting the same city may differ significantly in terms of population condition.

We investigated density changes of red fox and martens (stone marten and beech marten) in urban area of Warsaw in the years 2015-2021. Winter snow tracking was used for monitoring density in the whole city area and separately for different habitats within the city. The obtained results were compared with two earlier periods of snow tracking: 1976-1978 and 2005-2008 (Goszczyński J., unpbl.). The average fox density in the study period was 0.9 ind./km² and 0.3 ind./km² for martens. The highest densities of foxes was registered in forests, riparian and ruderal area, and the lowest in built up area, low-density housing and cemeteries. Martens reached the highest densities in allotment gardens and cemeteries, whilst in other habitats it was on a similar level. We found a decrease in density of both mesocarnivores in winter season 2017/2018 regardless of habitat type, although the densities were stable in forests and riparian area. The average density of both mesocarnivores rose gradually since 1970s in all habitat types, but in a quite stable proportion between habitats. In the first period (1976-1978), fox tracks were registered only in forests and riparian areas, while martens tracks, mostly in parks and forests. In the second period, tracks of these species were noted in each habitat type. In the third period, tracks were also present in each habitat, but their average number rose. Our results indicate progressive colonization of Warsaw and increase in abundance and population numbers of urban carnivores, continuously since 1970s. Independently of study period, the highest densities of mesocarnivores were recorded in forests and riparian area, what underlines the role of these habitats in development and maintenance of wildlife populations. The reasons of population decline in season 2017/2018 are not clear, but we suspect the potential role of sarcoptic mange in shaping population dynamics of urban carnivores.

REDD+ (Reduced emission from the deforestation and forest degradation) program has resulted in investigations of forest carbon from local to global scale. Forests play an important role in mitigating climate change as they acts as major stores of carbon. Leasehold forestry (LF) is a participatory model of forest management whereby degraded forest are handed over to groups of poor household to improve forest condition and reduce poverty. Sowing of grasses, fodder species and the promotion of natural regeneration have contributed towards increased plant diversity in leasehold forests. These restored forests might increase the provision of environmental services including carbon sequestration. However studies that have quantified the carbon stocks of leasehold forests is lacking. This study aims to quantify the carbon stock of leasehold forests and analyze its relationship with forest characteristics. Our study will be carried out in Nawalpur district which has long history of leasehold forestry. A total of 10 leasehold forests will be selected and field level data will be collected from concentric sample plots by taking 0.5% sampling intensity . At each plot, field inventory will be carried out to collect data on biomass of leaf litter, herbs and grass, regeneration status (species and its number), sapling status (species and dbh) and status of trees (species name, dbh, height and quality). Evidence of disturbances (grazing, lopping and fire) present on the plot will be noted. From this data, above ground carbon stock of leasehold forest will be quantified using appropriate allometric equations. Forest condition will be assessed by calculating regeneration density, sapling density, tree density and plant species diversity. Further, the relationship of carbon stock with forest condition and disturbances will be examined using multiple regression models. This study will provide baseline information on carbon sequestration potential of leasehold forests which will help in realizing the role of leasehold forestry in mitigating climate change.

The paper is interest to forest management & policy scientists and forest farmers, when deciding on the choice of frontier reforestation techniques. Today, the reforestation processes is considered either from a technical operational point of view or from an economic point of view in order to reduce costs. A multi-factor integrated approach will make it possible to make effective decisions and improve the efficiency of forest landscape restoration. The paper presents the development of an algorithm for managing the process of reforestation in modern conditions in order to optimize individual stages, the adequacy of the choice of the necessary operational technologies, taking into account the climatic and geomorphological characteristics of sites. Based on the initial data (characteristics of the area for reforestation), the algorithm will determine the optimal combination of reforestation stages, identify the stages that do not significantly affect the result of reforestation, determine the necessary operational technologies for previously defined stages, and determine the necessary reproductive material based on the selected operational technologies. Using this algorithm will reduce the ratio of reforestation costs by at least 15 %.

Automatic forest stock volume (FSV) estimation is crucial for carbon and water cycle prediction, assessing climate change, forest resources management, and ecosystem analysis. In recent years, various researches focused on this problem utilizing high-resolution light detection and ranging (LiDAR) data. However, this type of data requires unmanned autonomous vehicles (UAVs) to be collected. In practical application, it leads to high data collection costs. This paper considers computer vision approaches that estimate FSV using only freely available satellite images (Sentinel-2 with 10 meters per pixel spatial resolution). Therefore, the satellite-based approach needs neither additional hardware nor human resources for data collection. It makes the method scalable and allows application in hard-to-reach regions. We implemented and compared the classical machine learning approaches and deep convolutional neural networks (CNNs) for the FSV estimation task. For model training and evaluation, field-based measurements from the Russian boreal forest were used with a total area of about 20000 hectares. The result shows the high potential of computer vision methods for robust forest resources assessment.

This study aims to analyze the addition of widely available, cheap, and biologically-based 12 residues, such as charcoal fines, in the production of polymeric biocomposites reinforced with nat-13 ural fibers subjected to UV-c radiation. The addition of charcoal fines was 0, 10, 20, 30% in the pol-14 ymeric matrix of the biocomposites. Mechanical and chemical properties of the biocomposites were 15 evaluated. The flexural strength was more resistant when subjected to UV-c radiation with 20% 16 filling. These results attested that biocomposites with the addition of vegetative charcoal fines were 17 less susceptible to photodegradation.

Regional development is a valuable technique, strengthening the regional economy so as to create opportunities for new business activities and the exchange of knowledge and experience in addition to contributing to the improvement of human wellbeing. The EU is one of the most forest-rich regions; in fact, European forests are multifunctional and the sustainable management of natural resources should be established. National Forest Parks (NFP) represent protected areas with important forest diversity that play a critical role, both in reversing biodiversity loss and contributing to socio-economic development. The Cohesion Fund (CF) is one out of the five available European Structural and Investment Funds. The EU has implemented the CF during the 2014-2020 period on the Natura 2000 regions. The CF supports funding in transport and environmental projects in countries where the gross national income (GNI) per inhabitant is less than 90% of the EU average; in which fund Greece is included. To date in Greece, nine operational projects have been approved through the Cohesion Fund and concern six NFPs which are supervised by Management Bodies. Critical Assessment of the projects and policy analysis for their Management Bodies has been implemented. These findings demonstrate the existence of identical sustainability goals; the operational projects include four work packages aimed at more effective protection and conservation of biodiversity. The Cohesion Fund is a fundamental financing tool that may support the administration not only of these NFPs but also the 10 NFPs in Greece.

In Uruguay and neighbouring countries, Gleditsia triacanthos, known as honey locust, is an exotic tree species categorized as invasive; it produces severe ecological impact as it displaces native species, changing the structure of the native forest community. It is widely distributed, making it extremely difficult to control. One way to mitigate its negative impact is to identify opportunities to use it by revaluating its biological products. This work aims to study the applicability of this species as a source of both combustible and non-wood products, transforming it from a problem to a resource. In order to do this, the heat capacity, chemical composition and anatomical description of its wood was determined. Working on previous analysis of its extractives, autoclave extraction using water as a solvent was assayed in order to improve performance in a scalable and sustainable way. Polyphenols extracted by way of an adhesive for timber products were finally added, partially substituting petroleum derivatives. Extraction was improved by almost 20%, while the use on the adhesive showed promising results.

The degradation of forest areas in the Amazon region, where many indigenous communities live, has shown a marked deterioration in recent years. The Yasuní Biosphere Reserve (YBR), placed on the Ecuadorian Amazon and settled by several indigenous groups, is considered a hotspot of natural and cultural diversity. One of the most well-known communities in this region is the Kichwa, which is characterised by its traditional production systems, which in turn represents a means of subsistence and socio-ecological integration. In this study, we draw attention to the issue of forest cover management in the transition of cover zones on the YBR in the context of determining a relationship with anthropogenic activities. In our analysis, we use long-term vegetation data, from 2013 to 2020, and both Landsat 5 TM and Landsat 8 OLI/TIRS imagery to estimate changes in forest cover, grasslands, other lands and water, through a supervised classification technique that uses a random forest classification algorithm and a transition matrix. To determine the relationship between the Kichwa community sustainability indicator and vegetation changes, a multiple regression model was used which is based on a socio-productive survey completed by 133 Kichwa households. The results show that forest lost more than 11% of the areas between 2013 and 2020 and grasslands gained more than 10%. Annual changes in NDVI were mainly driven by land uses, economic viability and quality of life. This study is important in order to promote the continued use of green projects to address environmental change and improve the lives of indigenous communities.